Data processing systems such as the OLAP (OnLine Analytical Processing) System has been growing in popularity due to the increase in data volumes of information in business and the recognition of the value of business analysis. OLAP data processing provides a multidimensional conceptual view of the data, including full support for hierarchies, which is the most natural way to analyze businesses. For instance, an OLAP data processing model for sales evaluation can be organized as two dimensions: “geography” and “time.” A time dimension might contain levels of year, month, and day. Similarly, a geography dimension can represent country, state, and county or the like.
The OLAP System organizes facts in terms of dimensions which are ways that the facts can be categorized for analysis. OLAP data analysis system is a valuable and rewarding business intelligence tool in helping evaluate balanced scorecard targets, producing reports etc. This data analysis process allows users to find the rules and trends in the data, such as the most popular products purchased by certain group of people or in a certain region, and the sales results of a company or an industry.
To this end, OLAP data processing organizes the data according to dimensions in such a way that a so-called “cube” is created. An OLAP data cube is not strictly three-dimensional geometrically, but can have multiple dimensions greater or fewer than three dimensions. In other words, the term “data cube” is used just for the convenience of understanding and description. Its essence is to organize the data in multiple dimensional representation. Once the dimensions, which depend on the subjects to be analyzed and the analysis object, are determined, the frame of a data cube is formed. If the data cube happens to be three-dimensional and is represented with a chart or a drawing, then a visible cube can be obtained.
A data cube can be developed according to business units such as sales or marketing. A data cube may convert data into usable information by allowing data aggregation. With a data cube, a business user can slice and dice data at will according to the requirements of a business analysis.
In a word, OLAP data processing is valuable because of its flexibility and powerful business analysis ability. Once the facts and dimensions are defined within an OLAP data analysis server, data processing tools provide an easy way to analyze data by simply dragging and dropping dimensions and facts.
Currently, the method of constructing an OLAP data analysis model is to directly define the dimensions and measures that a data cube should have. Such a method only focuses on defining what dimensions are needed and ignores the relationship and structure existing among the dimensions. Moreover, it is hard for business people to reuse these dimensions. People may have to try very hard to find all useful dimensions for analysis when they design a data cube. What further complicates the problem is that there may be some dimensions which are time-dependent, e.g., the credit rating of a company. Most of the existing data analysis systems can not obtain correct analysis results when processing data cubes with time-dependent dimensions.